title: X-ray image enhancement via determinant based feature selection.
abstract: Previous work has investigated the feasibility of using Eigenimage-based enhancement tools to highlight abnormalities on chest X-rays (Butler et al in J Med Imaging Radiat Oncol 52:244-253, 2008). While promising, this approach has been limited by computational restrictions of standard clinical workstations, and uncertainty regarding what constitutes an adequate sample size. This paper suggests an alternative mathematical model to the above referenced singular value decomposition method, which can significantly reduce both the required sample size and the time needed to perform analysis. Using this approach images can be efficiently separated into normal and abnormal parts, with the potential for rapid highlighting of pathology.
authors.affiliation: School of Mathematics, The University of Edinburgh, Mayfield Road, Edinburgh, EH9 3JZ, Scotland, UK.
authors.forename: R
authors.initials: R
authors.lastname: Tappenden
authors.forename: J
authors.initials: J
authors.lastname: Hegarty
authors.forename: R
authors.initials: R
authors.lastname: Broughton
authors.forename: A
authors.initials: A
authors.lastname: Butler
authors.forename: I
authors.initials: I
authors.lastname: Coope
authors.forename: P
authors.initials: P
authors.lastname: Renaud
mesh_terms: D000465:Algorithms; D006801:Humans; D008175:Lung Neoplasms / Q000000981:diagnostic imaging; D011014:Pneumonia / Q000000981:diagnostic imaging; D011856:Radiographic Image Enhancement; D013902:Radiography, Thoracic; D014965:X-Rays